12 Frameworks for the
Agentic Experience Lifecycle

The framework that sits before delegation. Agentic AI replaces the traditional input-response loop with delegation - users now express intentions rather than give instructions. But autonomy without context invites misinterpretation. This framework covers the design of the pre-execution contract between human and agent: how users articulate goals versus mere instructions, how systems help users specify success criteria, constraints, exceptions, and what must never happen.

A structured approach to designing how humans grant, modify, and revoke authority in agentic systems. Covers scope definition, consent architecture, and revocation mechanisms. The grammar of giving authority - from standing orders to one-time mandates.

Trust is not a binary switch - it is a spectrum. This dynamic, user-adjustable system answers how much autonomous decision-making the agent should exercise, across which task types, and who controls that dial. It operates across three axes: consequence (cost of mistake), confidence (agent certainty), and familiarity (how well the agent knows this user's preferences).

A model for understanding and designing the ongoing negotiation between human confidence and agent reliability. Includes trust formation, maintenance, erosion, and recovery pathways. The architecture of earned confidence.

A catalogue of interrupt patterns for agentic systems - when to surface decisions to humans, how to present them, and how to calibrate frequency against consequence. The art of knowing when to speak and when to stay silent.

The architectural reality of enterprise agentic deployment: multiple agents collaborate, hand off tasks, and coordinate toward shared goals. This framework addresses the human experience of agent ecosystems - making the invisible choreography of multi-agent systems legible without overwhelming the user.

The design of what the agent knows about you over time - how that knowledge is encoded, persists across sessions and channels, how users can inspect and correct it, and how it is governed under data protection regulation. The personalisation and continuity layer.

A methodology for evaluating the quality of agentic experiences that unfold without human presence. Assesses integrity, outcome alignment, and return-state design. The discipline of judging what happened while you were away.

Making agent reasoning, decisions, and actions legible to humans in normal operation - not just at failure. The 'why did you do that?' layer. In regulated industries, this is not optional. Users must understand not just what the system did, but why it did it.

A design template for how agentic systems fail gracefully - including detection, communication, containment, recovery, and trust restoration. The kintsugi principle: failures repaired visibly become sources of deeper trust.

Traditional product onboarding teaches users how to navigate. Agentic onboarding must teach something fundamentally different: how to delegate. Users must form accurate mental models of agent capability, understand what kinds of goals the agent can pursue, and calibrate expectations before experiencing disappointment or misplaced trust.

The design of what the system should never do even when functioning perfectly - the encoding of organisational values, regulatory obligations, and ethical commitments into agentic behaviour as first-class design artefacts. Unlike failure architecture, which is reactive, this framework is constitutive. It defines the moral character of the agent system from the ground up.